Is AI the answer to big data analytics?

Is AI friend rather than foe when it comes to managing and making sense of the data deluge? We take a look…

Businesses – large or small – need data to power themselves, deliver insight on customers, partners, and employees, and to ensure they continue to make the right decisions about what to change about the past, what to keep that is current, and what to let go of or tweak for the future.

That said, data can be both a benefit and a problem. On the flip side of myriad of benefits on offer, the information deluge also presents huge challenges for businesses of all sizes.

Whether we like it or not, we all exist in the data economy. We’re willing to trade information and, often, personal details so long as there’s a value exchange. What that value is – or what price we can put on our own data – is an individual and unique decision (and, made much more complicated by the advent of GDPR, no doubt).

On average, around 6,000 tweets and 2,725,560 emails are sent every single second, while Google processes around 3.5 billion searches each day, according to Internet Live Stats.

What’s more, we’re dealing with both structured and unstructured data. Structured is just that – clearly defined so you know what you’re dealing with. Unstructured (which comprises the majority) is not so easy to recognise or make sense of. It’s a real data mess – and a headache to boot.

Many large organisations have data scientists at their disposal who are specifically skilled and trained in this area. Although this only goes part way to solving the problem. However, the majority – if not all – of smaller businesses, do not have such resources and instead the responsibility for making sense of the data falls to line of business heads or individual workers.

“Increased data growth over the past decade has created an unstructured data nightmare. It’s not just the cost to store it. Huge volumes of dark data make it harder to find what is useful and may mean we miss business opportunities,” said Alan Dayley, research director at Gartner.

“No matter which types of dark data your organisation collects, or how it is stored, the key to keeping data out of the dark is to ensure that you have a means of translating it from one form to another and ingesting it easily into whichever analytics platform you use.”

“More than 70% of employees have access to data they should not, and 80% of analysts’ time is spent simply discovering and preparing data.”

So, in a world where the amount of information being transmitted is increasing exponentially, how can smaller organisations, with all the constraints and challenges they already face, keep pace, let alone get ahead of the game?

Making sense of all the data

Given that businesses are faced with an increasing volume or information, formed of evermore complex data sets, it becomes even harder for them to derive value from it.

Big data analytics is nothing new, though. Many have understood the need to process large amounts of data for some time. The barrier, though, has been sheer ability vs intention. Often, with other pressing concerns, it’s hard for businesses to prioritise the analytics of data vs all the other tasks that need to occur to keep the corporate wheels moving. Similarly, faced with an incoming tidal wave of more data, it can be hard for mere humans to keep afloat.

Enter Artificial Intelligence (AI). It’s a contentious subject as it will, according to Gartner, remove 1.8 million jobs by 2020. But, by the same thinking, the analyst firm believes it will actually create 2.3 million jobs in the same period.

In a recent Cisco report entitled Transforming Businesses with Artificial Intelligence, the forecast was positive in terms of using AI for recruitment – 95% of recruiters surveyed said they believed AI would greatly increase talent acquisition and retention. Moreover, it’s firmly on the agenda: 83% of executives believe that AI is a strategic priority for their business today.

Ultimately, it will create more opportunities than it removes. Opportunities for larger businesses to put their best people to work on other value-added tasks and the chance for SMBs to level the talent playing field by using advanced technologies to fill their resource and knowledge gaps.

“Savvy small and medium businesses have already adopted a host of technologies to improve operational efficiency, from mobility and cloud computing to social media marketing tools. But that’s only the beginning; to remain relevant, profitable and efficient, it is essential to continue moving forward,” Cisco wrote in a Tech Connection blog recently.

“Both AI and machine learning enable computers to learn, predict patterns, and identify anomalies. While these technologies have been used by enterprises for some time to automate and optimise processes, save customers time and personalise the customer experience, they can be a newer concept for smaller businesses. Yet, they are just as critical, because they can help achieve the same sort of results.”

AI, for all the sophistication it offers, seems expensive and resource-intensive, at least initially. Just the thought of more work and budget going towards something non-core is likely to put most SMBs off.